Image segmentation is an image processing technique used in a wide variety of industries including medical image analysis, satellite imagery, visual surveillance and face recognition systems. Image segmentation partitions a digital image into multiple regions (i.e., sets of pixels) based on some homogeneity metric such as color, texture or contrast. This low-level abstraction provides high-level semantic operations with a reduced and relevant data set.
There are a number of existing techniques used for color image segmentation including feature-based, edge-based, region-based and hybrid segmentation approaches. Among the feature-based approaches, clustering techniques are the most popular. Clustering aims at grouping unlabeled image pixels into well-separated homogenous clusters to achieve a reduction in data. This grouping is performed in the image feature space utilizing some characteristic feature. While this method is efficient in segregating data based on global features, it ignores the spatial relationship of the image pixels. As a result, regions though spatially disconnected, end up having the same label. In addition, clustering requires the determination of cluster centroids and their number, which necessitates human supervision.
Use of histograms for segmentation is another feature-based technique wherein multilevel thresholding approaches are applied globally to the probability distribution function to separate regions of varying intensity or hue. This technique is sensitive to noise and requires the number of classes to be provided for segmentation.
Edge-based techniques utilize a threshold to determine binary pixel edges on a gradient or Laplacian map. In this way, an image is separated into regions based on changes in pixel intensity. In general, the generation of reliable edges is governed by the binarization threshold, which varies widely over different images. Consequently, the choice of a poor threshold results in disconnected edges or noisy pixels leading to regions with open contours. Post-processing techniques to fill gaps in disconnected edges is time-consuming while rendering them is undesirable.
Segmentation methods using region characteristics utilize spatial information along with intensity, texture or color information and ensure the formation of regions with closed boundaries. Seeded region growing uses a set of pixels as starting seeds, which are grown iteratively by grouping similar adjacent pixels to produce spatially coherent regions. The selection of seeds, however, influences the efficiency of segmentation and generally requires supervision.
Split-and-merge techniques start by recursively splitting non-homogenous regions until they results in homogenous regions. After the splitting stage, the split regions are merged using some similarity measure. The resulting segmentation, however, has imprecise boundaries due to blockiness.
Accordingly, the embodiments described hereinafter were developed in light of these and other drawbacks associated with known image segmentation techniques.